Graph databases have become increasingly popular for working with connected data from social networks, fraud detection, recommendations, and more. Unlike traditional relational databases, graph databases efficiently store relationships and can traverse highly associated datasets with ease.
In this article, we will explore 10 leading graph database solutions ideal for different use cases:
1. Cayley
Cayley is an open-source graph database built using Go that excels at knowledge representation and analytics behind search engines and contextual recommendations. Key features include:
- Support for multiple backend data stores
- Multiple query languages
- Modular architecture
- Easy horizontal scaling in Docker and Kubernetes
Cayley powers knowledge graphs at large tech companies like Google and is a flexible option to embed in applications needing graph capabilities.
2. Amazon Neptune
Amazon Neptune is a fully-managed graph database service optimized for billions of relationships and fast queries in a few milliseconds. Key highlights:
- Highly available and durable clustered design
- Tight integration with Amazon VPC, IAM, CloudWatch and other AWS services
- Secure through encryption, backups, SSL, and network isolation
- Query via open graph APIs like RDF, SPARQL, Gremlin
Due to robust capabilities and fully-managed nature, Neptune has been adopted by organizations like Comcast, Pearson, and FINRA for connected data challenges.
3. Neo4j
Neo4j is one of the most popular native graph databases, built in Java for querying highly connected data at scale. Features include:
- Declarative Cypher query language
- ACID transactions
- Advanced graph algorithms via procedure framework
- Drivers for Java, .NET, JavaScript and more
- Managed Neo4j Cloud available
With a developer-friendly querying approach, rich tooling, and language support, Neo4j has become a common choice to build graph-powered applications at Uber, Ebay, UBS and elsewhere.
4. ArangoDB
ArangoDB is a multi-model database supporting key-value, documents, and graph data models in one system. Key capabilities are:
- Flexible data storage and querying
- Advanced graph algorithms and traversals
- Great performance via native sharding
- Multi-datacenter replication for disaster recovery
- Secure user management and auditing
Companies like Comcast, Vodafone, and AlphaSense leverage ArangoDB‘s multi-model capabilities and resilience for knowledge management applications.
5. DataStax
DataStax supports massively scalable graph workloads by combining the indexing and scale-out of Apache Cassandra with support for property graph traversals and search. Benefits include:
- Leverage existing Cassandra operational skills & tools
- Industry-leading scale and throughput performance
- Multiple data models including key-value, graph, JSON docs, and more
- Integrates with existing data lakes and pipelines
The robust scalability makes DataStax a popular managed graph service for real-time applications at Gear4music, Entelgy, and ICE Mortgage Technology.
6. OrientDB
OrientDB seamlessly blends the agility of document databases with the power of graph databases into a unified, high-performance engine. Key aspects are:
- Documents stored as nodes and relationships as graph edges
- Multi-master distributed architecture
- ACID compliant transactions
- SQL-like query language
- Graph editor visualizations
- Wide programming language drivers
The flexibility of documents and graphs together has attracted user like MindValley, InterSystems, and more to OrientDB.
7. Dgraph
As a native graph database built for horizontal scaling in production via its unique "zero hop querying" architecture, Dgraph is designed from the ground up for low-latency queries on vast graph data. Key traits are:
- Purpose-built for speed, scale, concurrency with graph data
- Backend for applications using GraphQL frontends
- Live cluster data updates
- Rich access control rules
- Visualize query traces
Dgraph is a common choice of large organizations like Intuit, VMware, and MongoDB to power internal knowledge management solutions.
8. TigerGraph
TigerGraph excels at supporting real-time analytics on interconnected data leveraging parallel graph algorithms and AI-driven optimizations. Capabilities include:
- Ultra high performance 10x data compression
- Out-of-the-box support for fraud detection, recommendations etc
- Parallelized graph algorithm processing
- Continuous ingest from Kafka, Spark and other sources
- Available as fully managed cloud service
With robust analytics of relationships, TigerGraph powers fraud prevention at Intuit, supply chain insights at Jaguar Land Rover, and patient healthcare at UnitedHealth Group.
9. AllegroGraph
AllegroGraph utilizes entity-event knowledge graph technology specialized for decision intelligence use cases needing to derive connections within massive amounts of unstructured data. Key features consist of:
- Entity-event modeling for predictive insights
- Native sharding across data repositories
- Support for extremely large knowledge graphs
- Temporal and geospatial analytics
- Multi-tier storage optimizations
The advanced entity-event modeling approach had made AllegroGraph popular for applications in national security, finance, and healthcare at organizations like MITRE and Franz Inc.
10. Stardog
Stardog emphasizes end-to-end data virtualization to unite disparate data silos into an enterprise knowledge graph supporting a variety AI initiatives. Benefits encompass:
- Consolidate data without copying data lakes
- Leverage complex reasoning through AI engine
- Support graph SLAs with enterprise security
- Fuse real-time data streams with historical data
- Analyze data overlaps and gaps
The data virtualization and robust reasoning has led cutting-edge firms like Cisco, eBay, and NASA to leverage Stardog‘s smart knowledge capabilities.
In summary, these 10 graph databases represent leading options to support connected data apps with knowledge management, real-time recommendations, analytics, and more in different use cases. The best solution depends on your specific functional and scalability needs. By properly evaluating options against your requirements around relationships, querying complexity, and volume of nodes, you can select the ideal graph database to meet your needs whether on-premises or as-a-service in the cloud.
